metadata
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:283621
- loss:CachedMultipleNegativesRankingLoss
base_model: answerdotai/ModernBERT-base
widget:
- source_sentence: >-
// Uint is a helper routine that allocates a new uint value to store v and
// returns a pointer to it. This is useful when assigning optional
parameters.
sentences:
- "func (c *Animation) GetCurrentTimeWithParams(v *AnimationGetCurrentTimeParams) (float64, error) {\n\tresp, err := gcdmessage.SendCustomReturn(c.target, c.target.GetSendCh(), &gcdmessage.ParamRequest{Id: c.target.GetId(), Method: \"Animation.getCurrentTime\", Params: v})\n\tif err != nil {\n\t\treturn 0, err\n\t}\n\n\tvar chromeData struct {\n\t\tResult struct {\n\t\t\tCurrentTime float64\n\t\t}\n\t}\n\n\tif resp == nil {\n\t\treturn 0, &gcdmessage.ChromeEmptyResponseErr{}\n\t}\n\n\t// test if error first\n\tcerr := &gcdmessage.ChromeErrorResponse{}\n\tjson.Unmarshal(resp.Data, cerr)\n\tif cerr != nil && cerr.Error != nil {\n\t\treturn 0, &gcdmessage.ChromeRequestErr{Resp: cerr}\n\t}\n\n\tif err := json.Unmarshal(resp.Data, &chromeData); err != nil {\n\t\treturn 0, err\n\t}\n\n\treturn chromeData.Result.CurrentTime, nil\n}"
- "func Uint(v uint) *uint {\n\tp := new(uint)\n\t*p = v\n\treturn p\n}"
- |-
def after_init_app(self, app: FlaskUnchained):
"""
Configure the JSON encoder for Flask to be able to serialize Enums,
LocalProxy objects, and SQLAlchemy models.
"""
self.set_json_encoder(app)
app.before_first_request(self.register_model_resources)
- source_sentence: |-
Returns a template for the parent of this template.
@throws ValidationException if the template has no parent.
sentences:
- "func BodyContainsOr(values ...string) ResponseCondition {\n\treturn func(res *http.Response) error {\n\t\tbody, err := ioutil.ReadAll(res.Body)\n\t\tif err != nil {\n\t\t\treturn fmt.Errorf(\"failed to read response body: %s\", err)\n\t\t}\n\n\t\tfor _, value := range values {\n\t\t\tif strings.Contains(string(body), value) {\n\t\t\t\treturn nil\n\t\t\t}\n\t\t}\n\t\treturn fmt.Errorf(\"could not find '%v' in body '%s'\", values, string(body))\n\t}\n}"
- |-
protected function after_update($result) {
global $DB;
if (!$result) {
$this->beforeupdate = null;
return;
}
// The parent ID has changed, we need to fix all the paths of the children.
if ($this->beforeupdate->get('parentid') != $this->get('parentid')) {
$beforepath = $this->beforeupdate->get('path') . $this->get('id') . '/';
$like = $DB->sql_like('path', '?');
$likesearch = $DB->sql_like_escape($beforepath) . '%';
$table = '{' . self::TABLE . '}';
$sql = "UPDATE $table SET path = REPLACE(path, ?, ?) WHERE " . $like;
$DB->execute($sql, array(
$beforepath,
$this->get('path') . $this->get('id') . '/',
$likesearch
));
// Resolving sortorder holes left after changing parent.
$table = '{' . self::TABLE . '}';
$sql = "UPDATE $table SET sortorder = sortorder -1 "
. " WHERE competencyframeworkid = ? AND parentid = ? AND sortorder > ?";
$DB->execute($sql, array($this->get('competencyframeworkid'),
$this->beforeupdate->get('parentid'),
$this->beforeupdate->get('sortorder')
));
}
$this->beforeupdate = null;
}
- |-
public PathTemplate parentTemplate() {
int i = segments.size();
Segment seg = segments.get(--i);
if (seg.kind() == SegmentKind.END_BINDING) {
while (i > 0 && segments.get(--i).kind() != SegmentKind.BINDING) {}
}
if (i == 0) {
throw new ValidationException("template does not have a parent");
}
return new PathTemplate(segments.subList(0, i), urlEncoding);
}
- source_sentence: |-
Build a potentially nested fieldgroup
@param mixed $valueOrGroup Value of item, or title of group
@param string|array $titleOrOptions Title of item, or options in grouip
@return ArrayData Data for this item
sentences:
- |-
protected function getFieldOption($valueOrGroup, $titleOrOptions)
{
// Return flat option
if (!is_array($titleOrOptions)) {
return parent::getFieldOption($valueOrGroup, $titleOrOptions);
}
// Build children from options list
$options = new ArrayList();
foreach ($titleOrOptions as $childValue => $childTitle) {
$options->push($this->getFieldOption($childValue, $childTitle));
}
return new ArrayData(array(
'Title' => $valueOrGroup,
'Options' => $options
));
}
- |-
public static function minify($content, array $options = [])
{
$min = preg_replace(['/[\n\r]/', '/\>[^\S ]+/s', '/[^\S ]+\</s', '/(\s)+/s', ], ['', '>', '<', '\\1'], trim($content));
$min = str_replace(['> <'], ['><'], $min);
if (ArrayHelper::getValue($options, 'comments', false)) {
$min = preg_replace('/<!--(.*)-->/Uis', '', $min);
}
return $min;
}
- |-
private function loadXInclude(XInclude $xinclude, $filePath){
//load DOMDocument
$xml = new DOMDocument();
$loadSuccess = $xml->load($filePath);
$node = $xml->documentElement;
if($loadSuccess && !is_null($node)){
//parse the href content
$parser = new ParserFactory($xml);
$parser->loadContainerStatic($node, $xinclude->getBody());
}else{
throw new XIncludeException('Cannot load the XInclude DOM XML', $xinclude);
}
}
- source_sentence: |-
Check for new unread messages and send them to the custom api
@param client_id: ID of client user
sentences:
- |-
public function getLatMap()
{
if (null === $this->latMap) {
$this->latMap = $this->getTransliterationMap(Settings::ALPHABET_LAT);
}
return $this->latMap;
}
- |-
def check_new_messages(client_id):
"""Check for new unread messages and send them to the custom api
@param client_id: ID of client user
"""
# Return if driver is not defined or if whatsapp is not logged in.
# Stop the timer as well
if client_id not in drivers or not drivers[client_id] or not drivers[client_id].is_logged_in():
timers[client_id].stop()
return
# Acquire a lock on thread
if not acquire_semaphore(client_id, True):
return
try:
# Get all unread messages
res = drivers[client_id].get_unread()
# Mark all of them as seen
for message_group in res:
message_group.chat.send_seen()
# Release thread lock
release_semaphore(client_id)
# If we have new messages, do something with it
if res:
print(res)
except:
pass
finally:
# Release lock anyway, safekeeping
release_semaphore(client_id)
- |-
def get_uppermost_library_root_state(self):
"""Find state_copy of uppermost LibraryState
Method checks if there is a parent library root state and assigns it to be the current library root state till
there is no further parent library root state.
"""
library_root_state = self.get_next_upper_library_root_state()
parent_library_root_state = library_root_state
# initial a library root state has to be found and if there is no further parent root state
# parent_library_root_state and library_root_state are no more identical
while parent_library_root_state and library_root_state is parent_library_root_state:
if library_root_state:
parent_library_root_state = library_root_state.parent.get_next_upper_library_root_state()
if parent_library_root_state:
library_root_state = parent_library_root_state
return library_root_state
- source_sentence: If MultiTenantMiddleware is used, filter queryset by request.site_id
sentences:
- |-
def reduce_ticks(ax, which, maxticks=3):
"""Given a pyplot axis, resamples its `which`-axis ticks such that are at most
`maxticks` left.
Parameters
----------
ax : axis
The axis to adjust.
which : {'x' | 'y'}
Which axis to adjust.
maxticks : {3, int}
Maximum number of ticks to use.
Returns
-------
array
An array of the selected ticks.
"""
ticks = getattr(ax, 'get_{}ticks'.format(which))()
if len(ticks) > maxticks:
# make sure the left/right value is not at the edge
minax, maxax = getattr(ax, 'get_{}lim'.format(which))()
dw = abs(maxax-minax)/10.
start_idx, end_idx = 0, len(ticks)
if ticks[0] < minax + dw:
start_idx += 1
if ticks[-1] > maxax - dw:
end_idx -= 1
# get reduction factor
fac = int(len(ticks) / maxticks)
ticks = ticks[start_idx:end_idx:fac]
return ticks
- |-
function (isPublic, name, data, ttl, published_at, coreid) {
var rawFn = function (msg) {
try {
msg.setMaxAge(parseInt((ttl && (ttl >= 0)) ? ttl : 60));
if (published_at) {
msg.setTimestamp(moment(published_at).toDate());
}
}
catch (ex) {
logger.error("onCoreHeard - " + ex);
}
return msg;
};
var msgName = (isPublic) ? "PublicEvent" : "PrivateEvent";
var userID = (this.userID || "").toLowerCase() + "/";
name = (name) ? name.toString() : name;
if (name && name.indexOf && (name.indexOf(userID) == 0)) {
name = name.substring(userID.length);
}
data = (data) ? data.toString() : data;
this.sendNONTypeMessage(msgName, { event_name: name, _raw: rawFn }, data);
}
- |-
def get_queryset(self):
'''
If MultiTenantMiddleware is used, filter queryset by request.site_id
'''
queryset = super(PageList, self).get_queryset()
if hasattr(self.request, 'site_id'):
queryset = queryset.filter(site_id=self.request.site_id)
return queryset
datasets:
- benjamintli/code-retrieval-combined-v2
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on answerdotai/ModernBERT-base
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: eval
type: eval
metrics:
- type: cosine_accuracy@1
value: 0.873
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9366666666666666
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9543333333333334
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.973
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.873
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31222222222222223
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19086666666666663
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0973
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.873
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9366666666666666
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9543333333333334
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.973
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9240732170821061
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9082900793650796
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9093847853022148
name: Cosine Map@100
SentenceTransformer based on answerdotai/ModernBERT-base
This is a sentence-transformers model finetuned from answerdotai/ModernBERT-base on the code-retrieval-combined-v2 dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: answerdotai/ModernBERT-base
- Maximum Sequence Length: 1024 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
- Training Dataset:
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False, 'architecture': 'OptimizedModule'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("modernbert-code-v2")
# Run inference
queries = [
"If MultiTenantMiddleware is used, filter queryset by request.site_id",
]
documents = [
"def get_queryset(self):\n '''\n If MultiTenantMiddleware is used, filter queryset by request.site_id\n '''\n queryset = super(PageList, self).get_queryset()\n if hasattr(self.request, 'site_id'):\n queryset = queryset.filter(site_id=self.request.site_id)\n return queryset",
'def reduce_ticks(ax, which, maxticks=3):\n """Given a pyplot axis, resamples its `which`-axis ticks such that are at most\n `maxticks` left.\n\n Parameters\n ----------\n ax : axis\n The axis to adjust.\n which : {\'x\' | \'y\'}\n Which axis to adjust.\n maxticks : {3, int}\n Maximum number of ticks to use.\n\n Returns\n -------\n array\n An array of the selected ticks.\n """\n ticks = getattr(ax, \'get_{}ticks\'.format(which))()\n if len(ticks) > maxticks:\n # make sure the left/right value is not at the edge\n minax, maxax = getattr(ax, \'get_{}lim\'.format(which))()\n dw = abs(maxax-minax)/10.\n start_idx, end_idx = 0, len(ticks)\n if ticks[0] < minax + dw:\n start_idx += 1\n if ticks[-1] > maxax - dw:\n end_idx -= 1\n # get reduction factor\n fac = int(len(ticks) / maxticks)\n ticks = ticks[start_idx:end_idx:fac]\n return ticks',
'function (isPublic, name, data, ttl, published_at, coreid) {\n var rawFn = function (msg) {\n try {\n msg.setMaxAge(parseInt((ttl && (ttl >= 0)) ? ttl : 60));\n if (published_at) {\n msg.setTimestamp(moment(published_at).toDate());\n }\n }\n catch (ex) {\n logger.error("onCoreHeard - " + ex);\n }\n return msg;\n };\n\n var msgName = (isPublic) ? "PublicEvent" : "PrivateEvent";\n var userID = (this.userID || "").toLowerCase() + "/";\n name = (name) ? name.toString() : name;\n if (name && name.indexOf && (name.indexOf(userID) == 0)) {\n name = name.substring(userID.length);\n }\n\n data = (data) ? data.toString() : data;\n this.sendNONTypeMessage(msgName, { event_name: name, _raw: rawFn }, data);\n }',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 768] [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.9183, -0.0231, -0.0561]])
Evaluation
Metrics
Information Retrieval
- Dataset:
eval - Evaluated with
InformationRetrievalEvaluator
| Metric | Value |
|---|---|
| cosine_accuracy@1 | 0.873 |
| cosine_accuracy@3 | 0.9367 |
| cosine_accuracy@5 | 0.9543 |
| cosine_accuracy@10 | 0.973 |
| cosine_precision@1 | 0.873 |
| cosine_precision@3 | 0.3122 |
| cosine_precision@5 | 0.1909 |
| cosine_precision@10 | 0.0973 |
| cosine_recall@1 | 0.873 |
| cosine_recall@3 | 0.9367 |
| cosine_recall@5 | 0.9543 |
| cosine_recall@10 | 0.973 |
| cosine_ndcg@10 | 0.9241 |
| cosine_mrr@10 | 0.9083 |
| cosine_map@100 | 0.9094 |
Training Details
Training Dataset
code-retrieval-combined-v2
- Dataset: code-retrieval-combined-v2 at 2b971a6
- Size: 283,621 training samples
- Columns:
queryandpositive - Approximate statistics based on the first 1000 samples:
query positive type string string details - min: 5 tokens
- mean: 44.94 tokens
- max: 856 tokens
- min: 30 tokens
- mean: 181.2 tokens
- max: 1024 tokens
- Samples:
query positive Start the asyncio event loop and runs the application.def main():
"""Start the asyncio event loop and runs the application."""
# Helper method so that the coroutine exits cleanly if an exception
# happens (which would leave resources dangling)
async def _run_application(loop):
try:
return await cli_handler(loop)
except KeyboardInterrupt:
pass # User pressed Ctrl+C, just ignore it
except SystemExit:
pass # sys.exit() was used - do nothing
except: # pylint: disable=bare-except # noqa
import traceback
traceback.print_exc(file=sys.stderr)
sys.stderr.writelines(
'\n>>> An error occurred, full stack trace above\n')
return 1
try:
loop = asyncio.get_event_loop()
return loop.run_until_complete(_run_application(loop))
except KeyboardInterrupt:
pass
return 1Initialize the pool manager with the number of pools, the entry sizes for each
pool, and the maximum depth of the free pool.
@param bufferEntrySizes the memory sizes of each entry in the pools
@param bufferEntryDepths the maximum number of entries in the free poolpublic void initialize(int[] bufferEntrySizes, int[] bufferEntryDepths) {
if (TraceComponent.isAnyTracingEnabled() && tc.isEntryEnabled()) {
Tr.entry(tc, "initialize");
}
// order both lists from smallest to largest, based only on Entry Sizes
int len = bufferEntrySizes.length;
int[] bSizes = new int[len];
int[] bDepths = new int[len];
int sizeCompare;
int depth;
int sizeSort;
int j;
for (int i = 0; i < len; i++) {
sizeCompare = bufferEntrySizes[i];
depth = bufferEntryDepths[i];
// go backwards, for speed, since first Array List is
// probably already ordered small to large
for (j = i - 1; j >= 0; j--) {
sizeSort = bSizes[j];
if (sizeCompare > sizeSort) {
// add the bigger one after the smaller one
bSizes[j + 1] = sizeCompare;
bDepths[j ...// List lists all of the documents in an index. The documents are returned in
// increasing ID order.func (x *Index) List(c context.Context, opts *ListOptions) *Iterator {
t := &Iterator{
c: c,
index: x,
count: -1,
listInclusive: true,
more: moreList,
limit: -1,
}
if opts != nil {
t.listStartID = opts.StartID
if opts.Limit > 0 {
t.limit = opts.Limit
}
t.idsOnly = opts.IDsOnly
}
return t
} - Loss:
CachedMultipleNegativesRankingLosswith these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim", "mini_batch_size": 128, "gather_across_devices": false, "directions": [ "query_to_doc" ], "partition_mode": "joint", "hardness_mode": null, "hardness_strength": 0.0 }
Evaluation Dataset
code-retrieval-combined-v2
- Dataset: code-retrieval-combined-v2 at 2b971a6
- Size: 31,516 evaluation samples
- Columns:
queryandpositive - Approximate statistics based on the first 1000 samples:
query positive type string string details - min: 5 tokens
- mean: 42.73 tokens
- max: 834 tokens
- min: 30 tokens
- mean: 180.42 tokens
- max: 1024 tokens
- Samples:
query positive This gets the version of OpenALPR
:return: Version informationdef get_version(self):
"""
This gets the version of OpenALPR
:return: Version information
"""
ptr = self._get_version_func(self.alpr_pointer)
version_number = ctypes.cast(ptr, ctypes.c_char_p).value
version_number = _convert_from_charp(version_number)
self._free_json_mem_func(ctypes.c_void_p(ptr))
return version_numberRemove all unnecessary comments from a lexer or parser filepublic String stripUnnecessaryComments(String javaContent, AntlrOptions options) {
if (!options.isOptimizeCodeQuality()) {
return javaContent;
}
javaContent = stripMachineDependentPaths(javaContent);
if (options.isStripAllComments()) {
javaContent = stripAllComments(javaContent);
}
return javaContent;
}Serialize reply to array or JSON.
@param {Object} packet
@param {String} packet.method "get", "search", "post", "put", "delete", "sub", "unsub".
@param {String} packet.resource
@param {String} packet.id
@param {*} packet.body
@param {Number} [packet.status]
@param {Number|String} [packet.date]
@param {Object} [packet.headers]
@param {Boolean} [json] true to generate JSON instead of array.
@returns {Array|String|null}function reply(packet, json) {
return _create(packet, packet.status || 500, (METHODS[packet.method] || '') + packet.resource, json);
} - Loss:
CachedMultipleNegativesRankingLosswith these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim", "mini_batch_size": 128, "gather_across_devices": false, "directions": [ "query_to_doc" ], "partition_mode": "joint", "hardness_mode": null, "hardness_strength": 0.0 }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: stepsper_device_train_batch_size: 1024per_device_eval_batch_size: 1024learning_rate: 8e-05num_train_epochs: 1warmup_steps: 0.05bf16: Truedataloader_num_workers: 4load_best_model_at_end: Truepush_to_hub: Truehub_model_id: modernbert-code-v2batch_sampler: no_duplicates
All Hyperparameters
Click to expand
do_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 1024per_device_eval_batch_size: 1024gradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 8e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 1max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: Nonewarmup_ratio: Nonewarmup_steps: 0.05log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Trueenable_jit_checkpoint: Falsesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseuse_cpu: Falseseed: 42data_seed: Nonebf16: Truefp16: Falsebf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: -1ddp_backend: Nonedebug: []dataloader_drop_last: Falsedataloader_num_workers: 4dataloader_prefetch_factor: Nonedisable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Trueignore_data_skip: Falsefsdp: []fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}parallelism_config: Nonedeepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torch_fusedoptim_args: Nonegroup_by_length: Falselength_column_name: lengthproject: huggingfacetrackio_space_id: trackioddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Truepush_to_hub: Trueresume_from_checkpoint: Nonehub_model_id: modernbert-code-v2hub_strategy: every_savehub_private_repo: Nonehub_always_push: Falsehub_revision: Nonegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_for_metrics: []eval_do_concat_batches: Trueauto_find_batch_size: Falsefull_determinism: Falseddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Noneinclude_num_input_tokens_seen: noneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseliger_kernel_config: Noneeval_use_gather_object: Falseaverage_tokens_across_devices: Trueuse_cache: Falseprompts: Nonebatch_sampler: no_duplicatesmulti_dataset_batch_sampler: proportionalrouter_mapping: {}learning_rate_mapping: {}
Training Logs
| Epoch | Step | Training Loss | Validation Loss | eval_cosine_ndcg@10 |
|---|---|---|---|---|
| 0.0722 | 20 | 3.9983 | 1.3745 | 0.7545 |
| 0.1444 | 40 | 1.0297 | 0.7864 | 0.8493 |
| 0.2166 | 60 | 0.6830 | 0.5917 | 0.8833 |
| 0.2888 | 80 | 0.5476 | 0.5128 | 0.8973 |
| 0.3610 | 100 | 0.4891 | 0.4641 | 0.9028 |
| 0.4332 | 120 | 0.4436 | 0.4370 | 0.9098 |
| 0.5054 | 140 | 0.4304 | 0.4151 | 0.9154 |
| 0.5776 | 160 | 0.4101 | 0.3948 | 0.9161 |
| 0.6498 | 180 | 0.3910 | 0.3829 | 0.9190 |
| 0.7220 | 200 | 0.3794 | 0.3729 | 0.9188 |
| 0.7942 | 220 | 0.3668 | 0.3650 | 0.9207 |
| 0.8664 | 240 | 0.3683 | 0.3573 | 0.9230 |
| 0.9386 | 260 | 0.359 | 0.3534 | 0.9241 |
- The bold row denotes the saved checkpoint.
Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.3.0
- Transformers: 5.0.0
- PyTorch: 2.10.0+cu128
- Accelerate: 1.13.0
- Datasets: 4.0.0
- Tokenizers: 0.22.2
Citation
BibTeX
Sentence Transformers
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
CachedMultipleNegativesRankingLoss
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}